Learning temporal regularities of user behavior for anomaly detection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2052, Issue , 2001, Pages 143-152

  Seleznyov, Alexandr ^a   Mazhelis, Oleksiy ^a   Puuronen, Seppo ^a  

^a UNIVERSITY OF JYVÄSKYLÄ   (Finland)

Author keywords

Anomaly detection; Intrusion detection; Network security; Online learning; User profiling; User recognition

Indexed keywords

ANOMALY DETECTION; BEHAVIORAL RESEARCH; INTRUSION DETECTION; NETWORK ARCHITECTURE; SECURITY SYSTEMS;

ONLINE LEARNING; SECURITY INCIDENT; SEQUENTIAL INFORMATION; TEMPORAL FEATURES; TEMPORAL RELATION; USER BEHAVIORS; USER PROFILING; USER RECOGNITION;

NETWORK SECURITY;

EID: 84955605432     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45116-1_16     Document Type: Conference Paper

References (5)

1. Lunt, T. and Tamaru, A. and Gilham, F. and Jagannathan, R. and Neumann, P. and Javitz, H. and Valdes, A. and Garvey, T.: A Real-Time Intrusion detection Expert System (IDES) | Final Technical Report. Computer Science Laboratory, SRI International, Technical (1992)
2. Smaha, S.: Tools for Misuse Detection. ISSA'93, Crystal City, VA (1993)
3. Seleznyov, A. and Puuronen, S.: Anomaly Intrusion Detection Systems: Handling Temporal Relations between Events. 2nd International Workshop on Recent Advances in Intrusion Detection, Lafayette, Indiana, USA (1999)
4. Seleznyov, A. and Puuronen, S.: HIDSUR: A Hybrid Intrusion Detection System based on Real-time User Recognition. 11th International Workshop on Database and Expert Systems Applications, Greenwich-London, England (2000)
5. Allen, J.: Maintaining Knowledge About Temporal Intervals. Communications of the ACM, 26, (1983) 832-843